fuzzy.FDR.approx {BaySIC}R Documentation

Generate Approximate Fuzzy Rejection Probabilites

Description

For hypothesis tests with discrete reference distributions, obtains fuzzy rejection probabilites for a given level of false discovery rate control

Usage

fuzzy.FDR.approx(pprev, p, alpha, N)

Arguments

pprev

numeric vector of p-values of length l, corresponding to strict inequality of test statistic values in a one-sided test (i.e., P(T>t))

p

length l numeric vector of p-values corresponding to traditional one-sided test (i.e., P(T≥q t)).

alpha

FDR level of interest (under Benjamini-Hochberg FDR procedure)

N

Number of Monte Carlo samples used to generate fuzzy rejection probability approximations.

Details

This is a Monte Carlo implementation of the fuzzy FDR work developed by Kulinskaya et al. (2007)

Value

Returns a vector of length l corresponding to the fuzzy rejection probabilities of the hypotheses represented in pprev and p, under FDR level alpha

Author(s)

Nicholas B. Larson

References

http://www.bgx.org.uk/alex/Kulinskaya-Lewin-resubmit.pdf


[Package BaySIC version 1.0 Index]